[논문]A novel approach to dry weight adjustments for dialysis patients using machine learning

Kim, Hae Ri; Bae, Hong Jin; Jeon, Jae Wan; Ham, Young Rok; Na, Ki Ryang; Lee, Kang Wook; Hyon, Yun Kyong; Choi, Dae Eun

doi:10.1371/journal.pone.0250467

[해외논문] A novel approach to dry weight adjustments for dialysis patients using machine learning 원문보기

PLoS ONE, v.16 no.4, 2021년, pp.e0250467 -

Kim, Hae Ri (Division of Nephrology, Department of Internal Medicine, Chungnam National University Sejong Hospital, Sejong, South Korea) , Bae, Hong Jin (Division of Nephrology, Department of Internal Medicine, Cheongju St. Mary’s Hospital, Cheongju, South Korea) , Jeon, Jae Wan (Division of Nephrology, Department of Internal Medicine, Chungnam National University Sejong Hospital, Sejong, South Korea) , Ham, Young Rok (Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea) , Na, Ki Ryang (Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea) , Lee, Kang Wook (Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea) , Hyon, Yun Kyong (Medical Mathematics Division, National Institute for Mathematical Sciences, Daejeon, South Korea) , Choi, Dae Eun (Department of Nephrology, Medical School, Chungnam National University, Daejeon, South Korea)

Abstract ▼ AI-Helper

Background and aimsKnowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DWCP) using machine learning for patient’s clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight.MethodsAs a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DWCP data were collected when the dry weight was measured using the BIS (DWBIS). The gap between the two (GapDW) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg.ResultsBased on the gap between DWBIS and DWCP, 972, 303, and 384 patients were placed in groups with gaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seen that the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machine learning. As GapDW increases, it is more difficult to predict the target property. As GapDW increase, the mean values of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue index tended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellular water ratio tended to increase.ConclusionsMachine learning made it slightly easier to predict DWCP based on DWBIS under limited conditions and gave better insights into predicting DWCP. Malnutrition-related factors and ECW were important in reflecting the differences between DWBIS and DWCP.

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